We have evaluated Pattern-Net on the ModelNet40 dataset [1]} for the classification task. It contains 12311 meshed CAD models from 40 categories. Similar to the other work, 9843 models were used for training and the rest for testing and the models were normalized to a unit sphere. Each model is uniformly sampled from the mesh faces in 1k, 2k and 4k samples. During the training step, the points are augmented by randomly rotating, scaling and translating for being transformation invariant (Property ii above). The quantitative comparisons with the state-of-the-art point-based methods are presented in Table REF . Our method for 1k and 2k points is on par with the other methods and gives the best result for 4k points.
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